A growing number of organizations deploy multiple heterogeneous applications in infrastructures of distributed green data centers (DGDCs) to flexibly provide services to users around the world in a low-cost and high-quality way. The skyrocketing growth in types and number of heterogeneous applications dramatically increases the amount of energy consumed by DGDCs. The spatial and temporal variations in prices of power grid and availability of renewable energy make it highly challenging to minimize the energy cost of DGDC providers by intelligently scheduling arriving tasks of heterogeneous applications among GDCs while meeting their expected delay bound constraints. Unlike existing studies, this paper proposes a spatiotemporal task scheduling (STTS) algorithm to minimize energy cost by cost-effectively scheduling all arriving tasks to meet their delay bound constraints. STTS well investigates spatial and temporal variations in DGDCs. In each time slot, the energy cost minimization problem is formulated as a nonlinear constrained optimization one and addressed with the proposed genetic simulated-annealing-based particle swarm optimization. Trace-driven experiments show that STTS achieves larger throughput and lower energy cost than several typical task scheduling approaches while strictly meeting all tasks’ delay bound constraints. Note to Practitioners—This paper investigates the energy cost minimization problem for a DGDC provider while meeting delay bound constraints for all arriving tasks. Previous scheduling methods do not jointly consider spatial and temporal variations in prices of power grid and availability of renewable energy in DGDCs. Therefore, they fail to adopt such variations to minimize the energy cost of a DGDC provider. In this paper, a new method that avoids disadvantages of previous methods is proposed. It is realized by adopting a hybrid metaheuristic algorithm named GSP to solve a nonlinear constrained optimization problem. Experimental results...

中文翻译：

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HVAC 空气处理系统关键部件在组件和系统层面的故障预测


越来越多的组织在分布式绿色数据中心（DGDC）的基础设施中部署多种异构应用，以低成本、高质量的方式灵活地为全球用户提供服务。异构应用类型和数量的急剧增长极大地增加了 DGDC 的能源消耗量。电网价格和可再生能源可用性的时空变化使得通过智能调度 GDC 之间异构应用程序的到达任务同时满足其预期延迟约束来最小化 DGDC 提供商的能源成本变得极具挑战性。与现有研究不同，本文提出了一种时空任务调度（STTS）算法，通过经济有效地调度所有到达的任务以满足其延迟界限约束来最小化能量成本。 STTS 很好地研究了 DGDC 的空间和时间变化。在每个时隙中，能量成本最小化问题被表述为非线性约束优化问题，并通过所提出的基于遗传模拟退火的粒子群优化来解决。跟踪驱动的实验表明，STTS 比几种典型的任务调度方法实现了更大的吞吐量和更低的能源成本，同时严格满足所有任务的延迟界限约束。从业者注意事项——本文研究了 DGDC 提供商的能源成本最小化问题，同时满足所有到达任务的延迟限制约束。以往的调度方法没有共同考虑电网价格的时空变化和分布式发电中心可再生能源的可用性。因此，他们未能采用此类变化来最大限度地降低 DGDC 提供商的能源成本。 本文提出了一种避免以前方法缺点的新方法。它是通过采用混合元启发式算法GSP来解决非线性约束优化问题来实现的。实验结果...